SUMMARY Ingestion is a highly regulated behavior that integrates taste and hunger cues to balance food intake with metabolic needs. To study the dynamics of ingestion in the vinegar fly Drosophila melanogaster, we developed Expresso, an automated feeding assay that measures individual meal-bouts with high temporal resolution at nanoliter scale. Flies showed discrete, temporally precise ingestion that was regulated by hunger state and sucrose concentration. We identify 12 cholinergic local interneurons (IN1) necessary for this behavior. Sucrose ingestion caused a rapid and persistent increase in IN1 interneuron activity in fasted flies that decreased proportionally in response to subsequent feeding bouts. Sucrose responses of IN1 interneurons in fed flies were significantly smaller and lacked persistent activity. We propose that IN1 neurons monitor ingestion by connecting sugar-sensitive taste neurons in the pharynx to neural circuits that control the drive to ingest. Similar mechanisms for monitoring and regulating ingestion may exist in vertebrates.
This paper introduces a two-fly tracker which focuses on an approach to model and to solve occlusions as an optimization problem. Automated tracking of genetic model organisms is gaining importance since geneticists and neuroscientists have biological tools to systematically study the connection between genes, neurons and behaviour by performing large-scale behavioural experiments. This paper is about a fly tracker that provides automated quantification for such functional behaviour studies on Drosophila courtship behaviour. It enables measurement and visualization of behavioural differences in genetically modified fly pairs. The developed system provides solutions for all major challenges that were identified: arena detection, segmentation, quality control, resolving occlusions, resolving heading and detection of behaviour events. Among all challenges especially resolving occlusions turned out to be of particular importance and huge effort was invested to resolve that particular problem. Our tests show that our system is capable to identify flies through an entire video with an accuracy of 99.97%. This result is achieved by combining different types of local methods and modeling the global identity assignment as an optimization problem.
Mobile devices, like PDAs or smart phones, exhibit limited capabilities in terms of processing power and memory. Supported by advanced WLAN hotspot grid infrastructures, mobile terminals may enhance their computing capabilities significantly by utilizing remote resources in virtually shared spaces. Due to privacy and performance issues, the shared communication objects should be kept in proximity to the roaming owner which requires object migration.We propose a novel self-adaptive decision algorithm for object migration based on a cost-benefit function. This function considers parameters describing the expected latency caused by migration and the expected response time saved by local access. The importance of each parameter is determined by a weight and adapted using Bayesian concept learning. The feasibility of the approach is demonstrated by a prototypical implementation for PDAs based on the space-based middleware CORSO. We further investigate the decision algorithm by means of simulation.
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